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DTSTART;TZID=America/Detroit:20260317T160000
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SUMMARY:Mathematics - MICDE - MCAIM joint colloquium: Peter Bosler\, Sandia National Laboratories
DESCRIPTION:Bio:  Dr. Bosler received his B.S. degree with Honors in Oceanography from the U.S. Naval Academy in 2002. In 2002-2007\, he served as an officer in the U.S. Navy with active duty service that included both surface warfare and meteorology/oceanography operational support. Upon completing his service\, he started graduate studies at the University of Michigan and received a Ph.D. degree in Applied and Interdisciplinary Mathematics in 2013. In 2014\, he received the John von Neumann Postdoctoral Fellowship at Sandia National Laboratories\, and thereafter\, he became a staff member in the Center for Computing Research at Sandia. His projects involve close coupling between numerical methods development\, data collection\, application science\, and high-performance computing. Recent projects focus on climate modeling and plasma physics. Dr. Bosler received the Department of Energy Early Career Award for Advanced Scientific Computing in 2022 and the Presidential Early Career Award for Science and Engineering in 2025. \nAccelerating Earth System Simulation\nAbstract: Providing high-quality “actionable information” for strategic risk analysis is amongst the primary goals of the U.S. Department of Energy’s Exascale Earth System Model (E3SM). The simulation speed required to generate high-quality localized predictions at seasonal-to-decadal time scales is very high. In this talk\, we highlight some algorithmic design decisions that combine new research with classical numerical methods to enable E3SM’s ultra-high resolution configuration to achieve exascale performance and win the inaugural Gordon Bell Prize for Climate in 2023. Our design strategies tailor mathematical methods to both the unique features of the application space and to the heterogeneous computing architectures of exascale supercomputers. Ultimately\, these efforts doubled the speed of the most computationally demanding component of E3SM\, its atmosphere model. We will also discuss new and ongoing research associated with opportunities afforded by these performance gains. \n  \n\n  \nThe MICDE 2025-26 Seminar Series is open to all. \nGraduate Certificate in Computational Discovery and Engineering\, and MICDE fellows\, please use this form to record your attendance. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/math-micde-mcaim-peter-bosler-sandia/
LOCATION:1360 East Hall\, 530 Church St.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Climate and Space Sciences and Engineering,College Of Engineering,Featured Events,Mathematics,Mechanical Engineering,Micde,Micde Seminar,MICDE Seminar Series,Seminar
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DTSTART;TZID=America/Detroit:20180416T160000
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DTSTAMP:20230905T171419Z
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SUMMARY:MICDE Seminar: Vladimir Druskin\, Scientific Advisor\, Schlumberger Doll Research
DESCRIPTION:Bio: Vladimir Druskin is an applied mathematician with expertise in several areas including numerical algorithms\, large scale numerical simulations\, computational linear algebra\, inverse problems\, model reduction\, computational geophysics\, subsurface and medical imaging\, electrical engineering and financial mathematics. Dr. Druskin got his Ph.D. from Lomonosov Moscow State University (MSU) focused on applied mathematics. He is currently a scientific advisor at Schlumberger Doll Research working in energy research and development with demonstrated successful history of leading large collaborative industrial-academic projects in mathematical modeling and data-processing. \nReduced order models\, networks\, and applications to modeling and imaging with waves\nGeophysical seismic exploration\, as well as radar and sonar imaging require the solution of large-scale forward and inverse problems for hyperbolic systems of equations.  In this talk\, I will show how model order reduction can be used to address some intrinsic difficulties of these problems.  In model order reduction\, one approximates the response (transfer function) of a large-scale dynamical system using a smaller system\, called the reduced order model (ROM).  We consider ROMs that capture properties of the large problem that are essential for imaging and that can be realized via sparse graph-Laplacian networks.  The ROMs are data-driven\, i.e.\, they learn the underlying PDE problem from the transfer function.  One of the better-known applications of our ROMs is the efficient discretization of PDE problems in unbounded domains.  Here I will focus on two recent applications: (i) Multiscale modeling of elastic wave propagation via network approximations\, with low communication and computational cost; (ii) A direct\, nonlinear acoustic imaging algorithm in strongly heterogeneous media\, where the ROM is used to manipulate the data in such a way that multiply scattered waves are separated from the single scattered ones. \nDr. Druskin is being hosted by Prof. Borcea (Mathematics) and Prof. Schotland (Mathematics & Physics). If you would like to meet him\, please send an email to micde-contact@umich.edu
URL:https://micde.umich.edu/event/micde-seminar-vladimir-druskin-schlumberger-doll-research/
LOCATION:1360 East Hall\, 530 Church St.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events,MICDE Seminar Series
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DTSTART;TZID=UTC:20160913T161000
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SUMMARY:MICDE Seminar: Nathan Kutz\, University of Washington
DESCRIPTION:Bio: Nathan Kutz is the Robert Bolles and Yasuko Endo Professor in the department of Applied Mathematics\, and an adjunct professor of Electrical Engineering and Physics at the University of Washington. He was awarded the B.S. in Physics and Mathematics from the University of Washington in 1990 and the PhD in Applied Mathematics from Northwestern University in 1994. Following postdoctoral fellowships at the Institute for Mathematics and its Applications (University of Minnesota\, 1994-1995) and Princeton University (1995-1997)\, he joined the faculty of applied mathematics and served as Chair from 2007-2015. \nData-driven discovery of dynamical systems in the engineering\, physical and biological sciences\nWe demonstrate that the integration of data-driven dynamical systems and machine learning strategies with adaptive control are capable of producing efficient and optimal self-tuning algorithms for many complex systems arising in the engineering\, physical and biological sciences. We demonstrate that we can use emerging\, large-scale time-series data from modern sensors to directly construct\, in an adaptive manner\, governing equations\, even nonlinear dynamics\, that best model the system measured using sparsity-promoting techniques. Recent innovations also allow for handling multi-scale physics phenomenon and control protocols in an adaptive and robust way. The overall architecture is equation-free in that the dynamics and control protocols are discovered directly from data acquired from sensors. The theory developed is demonstrated on a number of example problems. Ultimately\, the method can be used to construct adaptive controllers which are capable of obtaining and maintaining optimal states while the machine learning and sparse sensing techniques characterize the system itself for rapid state identification and improved optimization. \nThis seminar is co-sponsored by the U-M Department of Mathematics.
URL:https://micde.umich.edu/event/micde-2016-fall-seminar-series-nathan-kutz-university-of-washington/
LOCATION:1360 East Hall\, 530 Church St.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:MICDE Seminar Series
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